Building-PCC: Building Point Cloud Completion Benchmarks
It addresses the challenge of incomplete 3D data in urban scenes for researchers in 3D geoinformation applications, but is incremental as it focuses on creating a benchmark rather than a new method.
This paper tackles the problem of incomplete point cloud data for urban buildings by establishing a new real-world benchmark dataset, Building-PCC, to evaluate deep learning methods for point cloud completion.
With the rapid advancement of 3D sensing technologies, obtaining 3D shape information of objects has become increasingly convenient. Lidar technology, with its capability to accurately capture the 3D information of objects at long distances, has been widely applied in the collection of 3D data in urban scenes. However, the collected point cloud data often exhibit incompleteness due to factors such as occlusion, signal absorption, and specular reflection. This paper explores the application of point cloud completion technologies in processing these incomplete data and establishes a new real-world benchmark Building-PCC dataset, to evaluate the performance of existing deep learning methods in the task of urban building point cloud completion. Through a comprehensive evaluation of different methods, we analyze the key challenges faced in building point cloud completion, aiming to promote innovation in the field of 3D geoinformation applications. Our source code is available at https://github.com/tudelft3d/Building-PCC-Building-Point-Cloud-Completion-Benchmarks.git.